Deep Learning Towards Intelligent Vehicle Fault Diagnosis

Abstract

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT.Index Terms-AI, deep learning, deep neural network, vehicle fault diagnosis, Internet of Things (IoT).

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

“…This data is then used to train artificial intelligence (AI) algorithms [31][32][33] to solve specific challenges in DDITS. Some of these challenges include vehicle theft detection [34], fault diagnosis [35], traffic object detection [36], tracking and recognition, traffic behavior analysis [37], amongst others. Despite extensive research on ITS and existing solutions, there are still challenges that need to be addressed.…”

Local & Federated Learning at the network edge for efficient predictive analytics

“…Real-time maintenance platforms cater to just-in-time supply chains, reducing stocking costs and lowering maintenance times by aggregating localized trained ML models from vehicle fleets. Estimation of State-of-Health and Remaining Useful Lifetime of individual components, e.g., HGV Batteries or combustion engines [5], [6], can be initially predicted in-vehicle and fused in a CL for further predictive maintenance analytics for the entire fleet. Moreover, localized fault diagnosis for detecting abnormal data merged with global outlier detection models has the potential of reducing maintenance expenditure of complex failures.…”

TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

“…Another area of application is on-board intelligent vehicle diagnosis [32]. By introducing ML mechanisms that could inspect different vehicle parameters, it will be able to perform self-inspections for prompt fault detection.…”